About • Publications • Full CV
I'm an undergraduate at Northeastern University double-majoring in computer science and mathematics. I'm interested in the formalization and verification of interesting and useful systems, as well as the logical foundations of mathematics and computing. My previous work involves system and network security. I'm thankful to have worked under the various supervisions of Cristina Nita-Rotaru at Northeastern University, Jelena Mirkovic at USC, and Christoph Haase at Oxford.
* indicates equal contribution.
Jacob Ginesin*, Max von Hippel*, Evan Defloor, Cristina Nita-Rotaru, Michael Tüxen. In submission to USENIX Security 2024.
We use formal methods to analyze the security of the Stream Control Transmission Protocol (SCTP). We report a symphony of new attacks across various attacker models, as well as the automated re-discovery of CVE-2021-3772.
Federico Cassano, Luisa Li, Akul Sethi, Noah Shinn, Abby Brennan-Jones, Jacob Ginesin, Edward Berman, George Chakhnashvili, Anton Lozhkov, Carolyn Jane Anderson, Arjun Guha. In submission to the Conference on Language Modeling, 2024.
We develop benchmarks to evaluate large language models on code editing performance, as previous benchmarks proved insufficient. We also fine-tune models for specifically code editing.
Jacob Ginesin, Christoph Haase. Presented at the Joint Mathematical Meetings 2024.
We define methods to compute the upper and lower boundaries of regular languages in order to speed up model checkers.
Federico Cassano, Charles Bershatsky, Jacob Ginesin, Sasha Bashenko. arXiv preprint arXiv:2305.06092, 2023.
A Return-oriented programming attack is when an attacker takes advantage of existing chunks of code in memory, dubbed gadgets, and chains them together to form an attack. We propose an approach to minimize the number of usable gadgets in compiled binaries, extending the methodology of a previous work.
Jacob Ginesin, Jelena Mirkovic. Presented at IEEE/ACM BDCAT2022
We study the validity of traffic at a DNS root server through analyzing historical data.